Analytical models in finance all share some basic concepts. Financial market participants observe some period of past events they deem relevant, build a statistical model of the observed data and then make the heroic assumption that events in the future will be like those in the past. While almost every financial institution has extensive risk modeling systems in place (as often mandated by regulators) the Global Financial Crisis has shown that such systems are frequently grossly inadequate. What is missing from nearly all models is an explicit recognition of how the present is different from the past, and therefore how the short term future is also likely to be different from the past. By defining “news” explicitly as the information set that informs us of the differences between past and present, we can condition our estimates of the distribution of future outcomes more robustly. Building upon the methods in diBartolomeo, Mitra, and Mitra (2009), and Kyle, Obizhaeva, Sinha and Tuzun (2012), we will introduce an approach to using quantified news flows and related sentiment scores in the prediction of asset portfolio risk. This process can operate in real time and currently addresses more than 50,000 global companies, sovereign credit entities, and financial institutions.